The present invention relates to dynamic optimization of system control over time. The need for dynamic optimization arises in many settings, as diverse as space ship control, solar car power consumption during a multi-day race, and retirement portfolio management. Generally, control actions at one time change the state of the system and optimum control actions at a later time. Control theory has broad applications. One of the classical papers in the early days of control theory was James Maxwell's paper “On governors” in the Proceedings of Royal Society, vol. 16 (1867-1868), which applied control theory to a machine governor, such as a centrifugal governor on a steam engine. One patent applies control theory to optimization of spacecraft trajectories and a spacecraft design, simultaneously. Wiffen, “Static/Dynamic Control for Optimizing a Useful Objective”, U.S. Pat. No. 6,496,741 B1. As practical applications of his particular control theory, Wiffen identifies in cols. 13-14 spacecraft trajectory, spacecraft design, groundwater or soil decontamination, stabilizing vibrations in structures, maximizing the value of a portfolio, electric circuit design, design and operation of chemical reactors and design and operation of a water reservoir system.
Consider the problem of a solar car in a multi-day race with legs that must be completed between 9 am and 5 pm each day. The course is set and the topography known. The available solar power varies with the time of day (deterministic) and cloud cover (probabilistic). Success on a given day can be measured by time to complete the course leg and the charge level of car batteries when the sun sets. Because the car is racing, the desired final state is completion of all legs of the race in the least time possible with a minimum final battery charge level. The primary control variable is power consumption. State variables include charge level, available solar power, distance traveled and road grade (which is a function of distance traveled, because the course is set). Speed follows from the selected power consumption and the road grade. This presents a non-trivial dynamic optimization problem, particularly in an area with intermittent cloud cover. It appears at first to be a simpler problem than retirement portfolio management, because choosing how far to run down the battery charge level, given the expected availability of recharging solar power, does not involve a trade-off between risk and reward, as does portfolio management. However, as a race day evolves, the a priori likelihood of cloud cover will be updated, which complicates the problem and favors a risk budget, margin of safety approach to charge level.
An opportunity arises to devise an approach to dynamic optimization that allows practical application to problems involving consumption over time and uncertain replenishment of resources. Improved strategy design, control decisions and system operation will follow.